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Lea Angelina
Universitas Muhammadiyah Semarang
Computer Science & IT Mathematics

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Forecasting Honda Car Retail Sales Using the Seasonal Autoregressive Integrated Moving Average Method: Peramalan Penjualan Retail Mobil Honda Menggunakan Metode Seasonal Autoregressive Integrated Moving Average Lea Angelina; Alia Permata; Jesicha Arsusma; Firochul Masichah; M. Al Haris; Ihsan Fathoni Amri
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.416

Abstract

This article discusses the forecasting of Honda car retail sales using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The study aims to forecast Honda car retail sales for the upcoming year. Various SARIMA models have been tested to determine the best model, and the results show that the SARIMA (1,1,0)(1,1,1)¹² model provides the lowest Mean Absolute Percentage Error (MAPE) among all tested models, which is 17,74%. Therefore, this model was chosen for forecasting sales over the next 12 months. The forecast results are expected to assist management in making optimal decisions regarding stock and marketing, as well as significantly enhancing operational efficiency and customer satisfaction in the future.
K-Nearest Neighbor Algorithm in Classification of Stunting Detection Dataset: Algoritma K-Nearest Neighbor dalam Klasifikasi Dataset Deteksi Stunting Lea Angelina; Saeful Amri; M Al Haris; Rochdi Wasono; Erna Julia Nanga; Faninda Aidina Fitri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.752

Abstract

Stunting is a nutritional problem that can affect children's physical growth and cognitive development and has a long-term impact on the quality of future generations. Early detection of stunting is crucial to enable timely and effective interventions. As technology advances, machine learning algorithms such as K-Nearest Neighbors (KNN) offer potential solutions to improve the accuracy of stunting risk classification. This study aims to design a classification model based on the K-Nearest Neighbors (KNN) algorithm in the early detection of stunting risk in toddlers. This research uses the 2024 stunting dataset obtained from Kaggle. The data is analyzed through the stages of cleaning, transformation, and division into training and testing data. The KNN model was tested with various K values to determine the optimal value. The results showed that the KNN model with a value of K=8 resulted in an accuracy of 93.80%, F1-Score of 93.65%, precision of 93.63%, and recall of 93.79%. This shows that KNN is reliable in classifying the nutritional status of toddlers and can be applied in stunting prevention efforts using more accurate data. This research contributes to developing machine learning-based classification systems that can support decision-making in public health programs, especially in reducing stunting rates.
ADASYN-Based Multiclass Support Vector Machine for Village Development Index Classification in North Maluku Province: Support Vector Machine Multikelas Berbasis ADASYN untuk Klasifikasi Indeks Pembangunan Desa di Provinsi Maluku Utara Tiani Wahyu Utami; Lea Angelina; Saeful Amri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1154

Abstract

Class imbalance is a significant constraint that can diminish the performance of classification models. This study implements the integration of Adaptive Synthetic Sampling (ADASYN) and Multiclass Support Vector Machine (SVM) to classify the 2024 Village Development Index (IDM) in North Maluku Province. The dataset comprises 684 villages, utilizing the Social Resilience Index (IKS), Economic Resilience Index (IKE), and Environmental Resilience Index (IKL) as predictor variables. The data was partitioned using a ratio of 80% for training and 20% for testing. An extreme imbalance was identified in the "independent village" category (0.88%); therefore, ADASYN was applied to the training data to generate 862 synthetic samples to balance the class distribution. The optimal model yielded by the process was a linear kernel SVM with a Cost value of 100, yielding an accuracy of 98.54%, precision of 98.26%, recall of 99.4%, and an F1-score of 98.83%. Of the total 137 villages evaluated, only two villages were misclassified: Salimuli Village and Dowongimaiti Village. These findings demonstrate the effectiveness of the ADASYN-SVM combination in producing accurate classifications to support village development policies in island regions.
Co-Authors Alia Permata Amri, Saeful Erna Julia Nanga Faninda Aidina Fitri Firochul Masichah Ihsan Fathoni Amri Jesicha Arsusma M. Al Haris Rochdi Wasono Tiani Wahyu Utami